# dependencies
library(tidyverse)
library(scales)
library(stringr)
library(effectsize)
library(janitor)
library(knitr)
library(kableExtra)load("../data/aiid/processed/AIID_subset_confirmatory.RData")
data_trimmed <- AIID_subset_confirmatory %>%
#filter(exclude_iat_stricter == FALSE) %>%
filter(english_fluency %in% c("English fluent - speak/read it regularly", "English is my primary language")) |>
dplyr::select(user_id,
domain,
age,
sex,
english_fluency,
exclude_iat_stricter,
block_order,
iat_type,
D,
# self-report attitudes data
prefer,
others_prefer,
actual_x, actual_y,
actual_diff,
gut_x, gut_y,
# individual differences scales data
individual_differences_measure,
individual_differences_sum_score)dat_subset <- data_trimmed |>
filter(domain %in% c("Poor People - Rich People",
"Conservatives - Liberals",
"Burger King - McDonald's",
"Protestants - Catholics"))
dat_r <- dat_subset |>
group_by(domain) |>
summarise(n = n(),
mean_prefer = mean(prefer, na.rm = TRUE),
mean_actual_diff = mean(actual_diff, na.rm = TRUE),
mean_actual_x = mean(actual_x, na.rm = TRUE),
mean_actual_y = mean(actual_y, na.rm = TRUE),
sd_prefer = sd(prefer, na.rm = TRUE),
sd_actual_diff = sd(actual_diff, na.rm = TRUE),
sd_actual_x = sd(actual_x, na.rm = TRUE),
sd_actual_y = sd(actual_y, na.rm = TRUE),
r_prefer_actual = broom::tidy(cor.test(actual_diff, prefer, use = "pairwise.complete.obs")),
r_actuals = broom::tidy(cor.test(actual_x, actual_y, use = "pairwise.complete.obs"))) |>
#r_gut = cor(gut_x, gut_y, use = "pairwise.complete.obs"),
#r_selfother = cor(prefer, others_prefer, use = "pairwise.complete.obs")) |>
unnest(r_prefer_actual, names_sep = "_") |>
unnest(r_actuals, names_sep = "_") |>
mutate_if(is.numeric, janitor::round_half_up, digits = 2) |>
select(domain, n,
mean_prefer, mean_actual_diff, mean_actual_x, mean_actual_y,
sd_prefer, sd_actual_diff, sd_actual_x, sd_actual_y,
r_prefer_actual = r_prefer_actual_estimate, r_prefer_actual_lower = r_prefer_actual_conf.low, r_prefer_actual_upper = r_prefer_actual_conf.high,
r_actuals = r_actuals_estimate, r_actuals_lower = r_actuals_conf.low, r_actuals_upper = r_actuals_conf.high)
# dat_r |>
# kable() |>
# kable_classic(full_width = FALSE)
dat_r |>
select(domain, n,
r_prefer_actual, r_actuals) |>
kable() |>
kable_classic(full_width = FALSE)| domain | n | r_prefer_actual | r_actuals |
|---|---|---|---|
| Burger King - McDonald’s | 1758 | 0.69 | 0.50 |
| Conservatives - Liberals | 1803 | 0.79 | -0.59 |
| Poor People - Rich People | 1891 | 0.53 | 0.06 |
| Protestants - Catholics | 1344 | 0.64 | 0.56 |
mean_sd_points <- dat_subset %>%
group_by(domain) %>%
summarise(
mean_x = mean(actual_x, na.rm = TRUE),
mean_y = mean(actual_y, na.rm = TRUE),
sd_x = sd(actual_x, na.rm = TRUE),
sd_y = sd(actual_y, na.rm = TRUE)
)
ggplot(dat_subset, aes(actual_x, actual_y)) +
geom_jitter(alpha = 0.4) +
# Red mean dot
geom_point(
data = mean_sd_points,
aes(x = mean_x, y = mean_y),
color = "red",
size = 3,
inherit.aes = FALSE
) +
# Optional: add SD error bars (crosshairs)
geom_errorbar(
data = mean_sd_points,
aes(x = mean_x, ymin = mean_y - sd_y, ymax = mean_y + sd_y),
color = "red",
width = 0,
inherit.aes = FALSE
) +
geom_errorbarh(
data = mean_sd_points,
aes(y = mean_y, xmin = mean_x - sd_x, xmax = mean_x + sd_x),
color = "red",
height = 0,
inherit.aes = FALSE
) +
facet_wrap(~ domain) +
scale_x_continuous(breaks = scales::breaks_pretty(10)) +
scale_y_continuous(breaks = scales::breaks_pretty(10)) +
coord_fixed() +
coord_cartesian(xlim = c(0.5, 10.5), ylim = c(0.5, 10.5))mean_sd_points <- dat_subset %>%
group_by(domain) %>%
summarise(
mean_y = mean(actual_diff, na.rm = TRUE),
mean_x = mean(prefer, na.rm = TRUE),
sd_y = sd(actual_diff, na.rm = TRUE),
sd_x = sd(prefer, na.rm = TRUE)
)
ggplot(dat_subset, aes(actual_diff, prefer)) +
geom_jitter(alpha = 0.4) +
# Red mean dot
geom_point(
data = mean_sd_points,
aes(x = mean_x, y = mean_y),
color = "red",
size = 3,
inherit.aes = FALSE
) +
# Optional: add SD error bars (crosshairs)
geom_errorbar(
data = mean_sd_points,
aes(x = mean_x, ymin = mean_y - sd_y, ymax = mean_y + sd_y),
color = "red",
width = 0,
inherit.aes = FALSE
) +
geom_errorbarh(
data = mean_sd_points,
aes(y = mean_y, xmin = mean_x - sd_x, xmax = mean_x + sd_x),
color = "red",
height = 0,
inherit.aes = FALSE
) +
facet_wrap(~ domain) +
scale_x_continuous(breaks = scales::breaks_pretty(10)) +
scale_y_continuous(breaks = scales::breaks_pretty(7)) +
coord_fixed() +
coord_cartesian(xlim = c(-10.5, 10.5), ylim = c(-3.5, +3.5))data_cohensd <- data_trimmed |>
group_by(domain) |>
summarize(n_x = sum(!is.na(actual_x)),
n_y = sum(!is.na(actual_y)),
mean_x = mean(actual_x, na.rm = TRUE),
mean_y = mean(actual_y, na.rm = TRUE),
sd_x = sd(actual_x, na.rm = TRUE),
sd_y = sd(actual_y, na.rm = TRUE),
d = cohens_d(actual_x, actual_y, pooled = TRUE)) |>
unnest(d) |>
# negative scores refer to preference for term on left and vice versa
mutate(Cohens_d = Cohens_d*-1) |>
rename(d = Cohens_d, ci_lower = CI_high, ci_upper = CI_low) |>
select(-CI) |>
mutate_if(is.numeric, round_half_up, digits = 2) |>
drop_na()
ggplot(data_cohensd, aes(mean_x, sd_x)) +
geom_point()library(tides)
data_cohensd |>
pivot_longer(
cols = c(n_x, n_y, mean_x, mean_y, sd_x, sd_y),
names_to = c(".value", "category"),
names_sep = "_"
) |>
mutate(min = 1,
max = 10,
n_items = 1,
digits = 2,
calculate_min_sd = TRUE,
method = "approximate") |>
tides_df(mean = mean,
sd = sd,
n = n,
min = min,
max = max,
n_items = n_items,
digits = digits,
calculate_min_sd = calculate_min_sd,
method = method) |>
plot_tides() +
geom_hline(yintercept = 4.5*.666, linetype = "dashed", color = "darkred")| domain | n_x | n_y | mean_x | mean_y | sd_x | sd_y | d | ci_upper | ci_lower |
|---|---|---|---|---|---|---|---|---|---|
| Denzel Washington - Tom Cruise | 1583 | 1582 | 7.64 | 5.10 | 1.83 | 2.47 | -1.17 | 1.09 | 1.25 |
| Gun Control - Gun Rights | 1169 | 1166 | 7.43 | 4.58 | 2.63 | 2.82 | -1.04 | 0.96 | 1.13 |
| Private - Public | 922 | 918 | 7.78 | 5.92 | 1.80 | 2.08 | -0.95 | 0.86 | 1.05 |
| Evolution - Creationism | 1354 | 1350 | 7.50 | 4.74 | 2.80 | 3.29 | -0.90 | 0.82 | 0.98 |
| Jazz - Teen Pop | 1264 | 1262 | 6.66 | 5.07 | 2.23 | 2.34 | -0.69 | 0.61 | 0.77 |
| Lawyers - Politicians | 919 | 922 | 5.72 | 4.53 | 2.00 | 1.91 | -0.61 | 0.51 | 0.70 |
| Mother Teresa - Princess Diana | 1071 | 1073 | 8.26 | 7.07 | 2.02 | 2.03 | -0.58 | 0.50 | 0.67 |
| Dogs - Cats | 1514 | 1515 | 8.04 | 6.70 | 2.09 | 2.75 | -0.55 | 0.47 | 0.62 |
| Reason - Emotions | 1290 | 1289 | 7.94 | 6.90 | 1.71 | 2.04 | -0.55 | 0.47 | 0.63 |
| Night - Morning | 1085 | 1084 | 7.34 | 6.24 | 1.87 | 2.44 | -0.51 | 0.42 | 0.59 |
| Wrinkles - Plastic Surgery | 965 | 962 | 5.27 | 4.21 | 2.10 | 2.23 | -0.49 | 0.40 | 0.58 |
| Pants - Skirts | 1342 | 1340 | 7.99 | 7.00 | 1.87 | 2.27 | -0.48 | 0.40 | 0.55 |
| Helpers - Leaders | 1217 | 1221 | 7.58 | 6.94 | 1.83 | 2.02 | -0.33 | 0.25 | 0.41 |
| Protein - Carbohydrates | 922 | 923 | 7.52 | 6.91 | 1.88 | 2.10 | -0.31 | 0.22 | 0.40 |
| Relaxing - Exercising | 1269 | 1268 | 7.76 | 7.15 | 1.99 | 2.18 | -0.29 | 0.21 | 0.37 |
| Canadian - American | 1341 | 1340 | 7.54 | 7.05 | 1.82 | 2.18 | -0.24 | 0.17 | 0.32 |
| Realism - Idealism | 896 | 893 | 7.20 | 6.76 | 1.86 | 2.01 | -0.23 | 0.14 | 0.32 |
| Bill Clinton - Hillary Clinton | 1062 | 1062 | 6.63 | 6.11 | 2.33 | 2.40 | -0.22 | 0.13 | 0.30 |
| Tall People - Short People | 1411 | 1417 | 7.22 | 6.80 | 1.85 | 1.90 | -0.22 | 0.15 | 0.30 |
| Redsox - Yankees | 1064 | 1060 | 5.74 | 5.31 | 2.01 | 2.24 | -0.20 | 0.12 | 0.29 |
| State - Church | 1064 | 1061 | 5.69 | 5.22 | 1.95 | 2.69 | -0.20 | 0.11 | 0.28 |
| 50 Cent - Britney Spears | 1180 | 1180 | 4.62 | 4.25 | 2.00 | 2.08 | -0.18 | 0.10 | 0.26 |
| Foreign Places - American Places | 1424 | 1422 | 7.94 | 7.61 | 1.73 | 1.88 | -0.18 | 0.11 | 0.26 |
| Protestants - Catholics | 952 | 955 | 6.20 | 5.83 | 2.03 | 2.18 | -0.18 | 0.09 | 0.27 |
| David Letterman - Jay Leno | 1043 | 1043 | 6.14 | 5.84 | 2.03 | 2.08 | -0.15 | 0.06 | 0.23 |
| Jews - Christians | 1443 | 1447 | 6.94 | 6.63 | 1.99 | 2.29 | -0.15 | 0.07 | 0.22 |
| Poor People - Rich People | 1441 | 1439 | 5.86 | 5.59 | 1.79 | 1.87 | -0.15 | 0.07 | 0.22 |
| Urban - Rural | 1040 | 1039 | 6.65 | 6.36 | 2.03 | 2.14 | -0.14 | 0.05 | 0.23 |
| Burger King - McDonald’s | 1335 | 1335 | 4.88 | 4.71 | 2.36 | 2.48 | -0.07 | -0.01 | 0.15 |
| Effort - Talent | 981 | 980 | 8.23 | 8.13 | 1.87 | 1.79 | -0.06 | -0.03 | 0.14 |
| Lord of the Rings - Harry Potter | 988 | 992 | 7.15 | 7.08 | 2.51 | 2.54 | -0.03 | -0.06 | 0.12 |
| Old People - Young People | 1436 | 1442 | 7.11 | 7.09 | 1.85 | 1.77 | -0.01 | -0.06 | 0.09 |
| Rebellious - Conforming | 1330 | 1329 | 5.48 | 5.45 | 2.04 | 2.17 | -0.01 | -0.06 | 0.09 |
| Friends - Family | 1079 | 1076 | 7.94 | 7.94 | 1.70 | 2.06 | 0.00 | -0.09 | 0.08 |
| Organized Labor - Management | 1067 | 1066 | 6.19 | 6.20 | 2.22 | 2.02 | 0.01 | -0.09 | 0.08 |
| Microsoft - Apple | 1187 | 1186 | 6.48 | 6.53 | 2.26 | 2.14 | 0.03 | -0.11 | 0.06 |
| New York - California | 1309 | 1308 | 6.85 | 6.91 | 2.06 | 1.95 | 0.03 | -0.11 | 0.05 |
| West Coast - East Coast | 930 | 929 | 7.11 | 7.18 | 1.97 | 1.98 | 0.04 | -0.13 | 0.06 |
| Asians - Whites | 1584 | 1587 | 7.22 | 7.41 | 1.90 | 1.88 | 0.10 | -0.17 | -0.03 |
| African Americans - European Americans | 1712 | 1714 | 7.09 | 7.30 | 1.89 | 1.83 | 0.11 | -0.18 | -0.05 |
| Meg Ryan - Julia Roberts | 1355 | 1354 | 6.75 | 7.08 | 2.00 | 2.05 | 0.16 | -0.24 | -0.09 |
| Artists - Musicians | 1029 | 1031 | 7.72 | 8.02 | 1.87 | 1.73 | 0.17 | -0.25 | -0.08 |
| Difficult - Simple | 1234 | 1229 | 6.37 | 6.75 | 2.06 | 2.22 | 0.18 | -0.25 | -0.10 |
| Coffee - Tea | 1332 | 1337 | 6.77 | 7.27 | 2.80 | 2.34 | 0.19 | -0.27 | -0.12 |
| Mountains - Ocean | 1101 | 1095 | 7.86 | 8.24 | 1.89 | 1.86 | 0.20 | -0.29 | -0.12 |
| Japan - United States | 1393 | 1391 | 6.72 | 7.14 | 1.78 | 2.17 | 0.21 | -0.29 | -0.14 |
| Stable - Flexible | 1194 | 1198 | 7.22 | 7.65 | 1.85 | 1.78 | 0.24 | -0.32 | -0.16 |
| Atheism - Religion | 1440 | 1437 | 5.18 | 5.88 | 2.74 | 2.67 | 0.26 | -0.33 | -0.19 |
| Dramas - Comedies | 1004 | 1003 | 7.16 | 7.71 | 1.90 | 1.93 | 0.29 | -0.38 | -0.20 |
| Pepsi - Coke | 1403 | 1402 | 5.67 | 6.44 | 2.55 | 2.59 | 0.30 | -0.37 | -0.23 |
| Southerners - Northerners | 1193 | 1193 | 6.43 | 7.05 | 2.01 | 1.80 | 0.32 | -0.40 | -0.24 |
| Strong - Sensitive | 968 | 966 | 6.54 | 7.20 | 1.95 | 1.89 | 0.34 | -0.43 | -0.25 |
| Cold - Hot | 1279 | 1280 | 5.62 | 6.31 | 1.97 | 1.83 | 0.36 | -0.44 | -0.28 |
| Skeptical - Trusting | 964 | 969 | 6.13 | 6.92 | 2.16 | 2.21 | 0.36 | -0.45 | -0.27 |
| Numbers - Letters | 1342 | 1344 | 6.62 | 7.45 | 2.25 | 2.02 | 0.39 | -0.46 | -0.31 |
| Rich People - Beautiful People | 1112 | 1110 | 5.75 | 6.51 | 1.86 | 1.81 | 0.42 | -0.50 | -0.33 |
| Receiving - Giving | 941 | 943 | 7.52 | 8.33 | 1.98 | 1.72 | 0.43 | -0.52 | -0.34 |
| Tax Reductions - Social Programs | 1126 | 1127 | 6.35 | 7.41 | 2.54 | 2.25 | 0.44 | -0.53 | -0.36 |
| Muslims - Jews | 1392 | 1392 | 6.12 | 7.06 | 2.18 | 2.01 | 0.45 | -0.52 | -0.37 |
| Meat - Vegetables | 1462 | 1463 | 6.68 | 7.84 | 2.61 | 2.11 | 0.49 | -0.56 | -0.42 |
| Traditional Values - Feminism | 1343 | 1345 | 5.56 | 6.80 | 2.49 | 2.27 | 0.52 | -0.60 | -0.44 |
| Career - Family | 1346 | 1349 | 6.89 | 8.02 | 2.05 | 1.89 | 0.57 | -0.65 | -0.50 |
| Team - Individual | 975 | 974 | 6.61 | 7.80 | 2.19 | 1.78 | 0.59 | -0.69 | -0.50 |
| Past - Future | 1332 | 1331 | 6.27 | 7.49 | 2.14 | 1.92 | 0.60 | -0.68 | -0.53 |
| Gay People - Straight People | 1688 | 1686 | 7.09 | 8.35 | 2.37 | 1.76 | 0.61 | -0.67 | -0.54 |
| Single - Married | 1410 | 1406 | 5.94 | 7.28 | 2.22 | 2.21 | 0.61 | -0.68 | -0.53 |
| Hiphop - Classical | 1398 | 1396 | 5.53 | 7.02 | 2.55 | 2.27 | 0.62 | -0.70 | -0.54 |
| Jocks - Nerds | 1334 | 1330 | 5.62 | 6.84 | 2.07 | 1.85 | 0.62 | -0.70 | -0.54 |
| Prolife - Prochoice | 1299 | 1294 | 5.22 | 7.12 | 3.00 | 2.92 | 0.64 | -0.72 | -0.57 |
| Technology - Nature | 1033 | 1033 | 7.41 | 8.52 | 1.85 | 1.59 | 0.64 | -0.73 | -0.55 |
| Kobe - Shaq | 957 | 956 | 5.05 | 6.39 | 2.06 | 1.93 | 0.67 | -0.77 | -0.58 |
| Tradition - Progress | 956 | 955 | 6.53 | 7.82 | 2.06 | 1.75 | 0.67 | -0.77 | -0.58 |
| Athletic People - Intelligent People | 1082 | 1079 | 7.22 | 8.46 | 1.97 | 1.53 | 0.70 | -0.79 | -0.61 |
| Security - Freedom | 1252 | 1249 | 7.25 | 8.59 | 2.11 | 1.62 | 0.71 | -0.79 | -0.63 |
| Fat People - Thin People | 1505 | 1506 | 5.48 | 6.85 | 1.94 | 1.72 | 0.75 | -0.82 | -0.68 |
| Capital Punishment - Imprisonment | 1304 | 1303 | 4.30 | 6.37 | 2.99 | 2.46 | 0.76 | -0.84 | -0.68 |
| Money - Love | 1199 | 1203 | 7.15 | 8.60 | 2.07 | 1.73 | 0.76 | -0.85 | -0.68 |
| Solitude - Companionship | 1033 | 1032 | 6.20 | 7.73 | 2.18 | 1.78 | 0.77 | -0.86 | -0.68 |
| Winter - Summer | 1425 | 1425 | 5.66 | 7.54 | 2.35 | 2.13 | 0.84 | -0.92 | -0.76 |
| Briefs - Boxers | 1010 | 1011 | 5.32 | 7.28 | 2.40 | 2.16 | 0.86 | -0.95 | -0.76 |
| Drinking - Abstaining | 1300 | 1299 | 4.72 | 6.91 | 2.24 | 2.25 | 0.97 | -1.05 | -0.89 |
| George Bush - John Kerry | 1382 | 1381 | 3.32 | 5.80 | 2.66 | 2.26 | 1.01 | -1.09 | -0.93 |
| Conservatives - Liberals | 1186 | 1188 | 4.50 | 6.81 | 2.31 | 2.24 | 1.02 | -1.10 | -0.93 |
| Manufactured - Natural | 894 | 897 | 6.22 | 8.06 | 1.89 | 1.71 | 1.02 | -1.12 | -0.93 |
| Republicans - Democrats | 1249 | 1249 | 4.28 | 6.56 | 2.34 | 2.11 | 1.02 | -1.11 | -0.94 |
| Speed - Accuracy | 1376 | 1379 | 6.79 | 8.57 | 1.87 | 1.57 | 1.04 | -1.12 | -0.96 |
| Innocence - Wisdom | 1049 | 1046 | 6.92 | 8.81 | 2.10 | 1.43 | 1.06 | -1.15 | -0.96 |
| Corporations - Nonprofits | 970 | 967 | 5.37 | 7.61 | 2.19 | 1.89 | 1.09 | -1.19 | -1.00 |
| Television - Books | 1502 | 1502 | 6.32 | 8.55 | 2.25 | 1.84 | 1.09 | -1.16 | -1.01 |
| Avoiding - Approaching | 1123 | 1120 | 4.59 | 7.13 | 2.12 | 1.90 | 1.26 | -1.35 | -1.17 |
| Astrology - Science | 997 | 996 | 4.85 | 8.06 | 2.58 | 2.01 | 1.39 | -1.49 | -1.29 |
| National Defense - Education | 1336 | 1334 | 5.93 | 8.91 | 2.47 | 1.60 | 1.43 | -1.51 | -1.34 |
| Punishment - Forgiveness | 985 | 989 | 5.07 | 8.05 | 2.32 | 1.80 | 1.44 | -1.54 | -1.34 |
| Determinism - Free will | 943 | 943 | 4.75 | 8.21 | 2.45 | 1.81 | 1.61 | -1.71 | -1.50 |
| Chaos - Order | 1055 | 1053 | 4.04 | 7.56 | 2.17 | 1.83 | 1.75 | -1.85 | -1.65 |
data_cohensd |>
summarise(
percentile = c(1, 5, 10, 25, 50, 75, 90, 95, 99) / 100,
d = map_dbl(percentile, ~ quantile(d, probs = .x, na.rm = TRUE)),
.groups = "drop"
) |>
mutate(percentile = percentile * 100,
d = round_half_up(d, 2)) |>
kable() |>
kable_classic(full_width = FALSE)| percentile | d |
|---|---|
| 1 | -1.05 |
| 5 | -0.63 |
| 10 | -0.50 |
| 25 | -0.17 |
| 50 | 0.26 |
| 75 | 0.67 |
| 90 | 1.03 |
| 95 | 1.30 |
| 99 | 1.62 |
Largest correlations
dat_subset <- AIID_subset_confirmatory |>
filter(english_fluency %in% c("English fluent - speak/read it regularly", "English is my primary language")) |>
filter(complete_individual_differences_data == TRUE) |>
select(bfi_o1, bfi_o5, bfi_e1, bfi_e5, bfi_n1, bfi_n4) |>
select(starts_with("bfi", ignore.case = FALSE))
dat_subset |>
summarize(n_first_half = sum(!is.na(bfi_e1)),
n_second_half = sum(!is.na(bfi_o1))) |>
kable() |>
kable_classic(full_width = FALSE)| n_first_half | n_second_half |
|---|---|
| 5191 | 5221 |
mat <- dat_subset |>
cor(use = "pairwise.complete.obs") |>
janitor::round_half_up(2)
mat |>
kable() |>
kable_classic(full_width = FALSE)| bfi_o1 | bfi_o5 | bfi_e1 | bfi_e5 | bfi_n1 | bfi_n4 | |
|---|---|---|---|---|---|---|
| bfi_o1 | 1.00 | 0.62 | NA | NA | NA | NA |
| bfi_o5 | 0.62 | 1.00 | NA | NA | NA | NA |
| bfi_e1 | NA | NA | 1.00 | 0.61 | -0.14 | 0.03 |
| bfi_e5 | NA | NA | 0.61 | 1.00 | -0.20 | -0.09 |
| bfi_n1 | NA | NA | -0.14 | -0.20 | 1.00 | 0.40 |
| bfi_n4 | NA | NA | 0.03 | -0.09 | 0.40 | 1.00 |
in US adults, 2008-2012. NHANES CDC data.
library(NHANES)
data(NHANES)
dat <- NHANES |>
filter(Age >= 18) |>
filter(!is.na(Height) & !is.na(Weight))
ggplot(dat, aes(Weight, Height)) +
geom_point(alpha = 0.1)dat |>
summarize(n = n(),
cor = broom::tidy(cor.test(Weight, Height, use = "pairwise.complete.obs"))) |>
unnest(cor) |>
select(n, r = estimate, ci_lower = conf.low, ci_upper = conf.high) |>
mutate_if(is.numeric, janitor::round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| n | r | ci_lower | ci_upper |
|---|---|---|---|
| 7414 | 0.45 | 0.43 | 0.47 |
dat |>
group_by(Gender) |>
summarize(n = n(),
cor = broom::tidy(cor.test(Weight, Height, use = "pairwise.complete.obs"))) |>
unnest(cor) |>
select(n, r = estimate, ci_lower = conf.low, ci_upper = conf.high) |>
mutate_if(is.numeric, janitor::round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| n | r | ci_lower | ci_upper |
|---|---|---|---|
| 3763 | 0.28 | 0.25 | 0.31 |
| 3651 | 0.39 | 0.36 | 0.42 |
dat <- NHANES |>
filter(Age >= 18) |>
filter(!is.na(Gender) & !is.na(Height) & !is.na(Weight))
dat |>
summarize(n = n()) |>
kable() |>
kable_classic(full_width = FALSE)| n |
|---|
| 7414 |
Weight:
##
## Cohen's d
##
## d estimate: -0.6685073 (medium)
## 95 percent confidence interval:
## lower upper
## -0.7152995 -0.6217151
Height:
##
## Cohen's d
##
## d estimate: -1.869864 (large)
## 95 percent confidence interval:
## lower upper
## -1.924451 -1.815276